Fix Unexpceted Unexpected, etc

paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.cc

@@ -49,8 +49,8 @@ FastThreadedSSAGraphExecutor::FastThreadedSSAGraphExecutor(
       /*disable_setting_default_stream_for_allocator=*/true,
       /*stream_priority=*/0);
   if (ir::IsTopologySortOperationsUnique(*graph_)) {
-    VLOG(10)
-        << "Change thread number to 1 because the toposort order is unique";
+    VLOG(10) << "Change thread number to 1 because the topology sort order is "
+                "unique";
     strategy_.num_threads_ = 1;
     traced_ops_.clear();
     for (auto *op_node : TopologySortOperations(*graph_)) {

paddle/fluid/framework/details/fetch_op_handle.cc

@@ -39,7 +39,7 @@ FetchOpHandle::~FetchOpHandle() = default;
 
 void FetchOpHandle::RecordWaitEventOnCtx(platform::DeviceContext *waited_ctx) {
   PADDLE_THROW(platform::errors::PermissionDenied(
-      "No nodes need to wait FetchOp. Unexpceted Error."));
+      "No nodes need to wait FetchOp. Unexpected Error."));
 }
 
 static void CheckDims(const framework::DDim &tensor_dims,

paddle/fluid/framework/operator.cc

@@ -2037,7 +2037,7 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
       phi::KernelContext phi_kernel_context;
       if (enable_cache_runtime_context_ && !need_prepare_phi_data_ &&
           !need_prepare_data_) {
-        // TODO(inference): Now we only suppor dense_tensor cache, we may be
+        // TODO(inference): Now we only support dense_tensor cache, we may be
         // support ScalarTensor, SparseTensor in future.
         bool all_dense_tensor_input_{true};
         for (auto& iter : Inputs()) {
@@ -2572,7 +2572,7 @@ Scope* OperatorWithKernel::PrepareData(
         // for some situation like InferShape().
         // In this situation We cannot skip Var analysis, as
         // oneDNN shape of Var may differ from kNHWC Var
-        // In such situation corressponding resized Var
+        // In such situation corresponding resized Var
         // has to be created and registered
         if ((tensor_in->layout() == DataLayout::ONEDNN) &&
             (var->IsType<phi::DenseTensor>() == true) &&
@@ -3192,7 +3192,7 @@ void OperatorWithKernel::BuildPhiKernelContext(
   for (size_t i = 0; i < input_names.size(); ++i) {
     auto it = ctx.inputs.find(input_names[i]);
 
-    // calcute the start and end index of the input tensors
+    // calculate the start and end index of the input tensors
     size_t start_idx =
         (i == 0 ? 0 : phi_kernel_context->InputRangeAt(i - 1).second);
     // deal with optional here
@@ -3398,7 +3398,7 @@ void OperatorWithKernel::BuildPhiKernelContext(
             attr_iter,
             Attrs().end(),
             platform::errors::NotFound("(%s) is not found in AttributeMap when "
-                                       "buildind static KernelContext.",
+                                       "building static KernelContext.",
                                        attr_names[i]));
         switch (AttrTypeID(attr_iter->second)) {
           case proto::AttrType::INTS: {
@@ -3472,7 +3472,7 @@ void OperatorWithKernel::BuildPhiKernelContext(
                             RuntimeAttrs().end(),
                             platform::errors::NotFound(
                                 "(%s) is not found in AttributeMap when "
-                                "buildind static KernelContext.",
+                                "building static KernelContext.",
                                 attr_names[i]));
         }
 

paddle/fluid/framework/parallel_executor.cc

@@ -639,15 +639,15 @@ void InitP2P(const std::vector<platform::Place> &places) {
     for (int i = 0; i < count; ++i) {
       for (int j = 0; j < count; ++j) {
         if (devices[i] == devices[j]) continue;
-        int can_acess = -1;
+        int can_access = -1;
 #ifdef PADDLE_WITH_HIP
         hipError_t ret =
-            hipDeviceCanAccessPeer(&can_acess, devices[i], devices[j]);
-        if (ret != hipSuccess || can_acess != 1) {
+            hipDeviceCanAccessPeer(&can_access, devices[i], devices[j]);
+        if (ret != hipSuccess || can_access != 1) {
 #else
         cudaError_t ret =
-            cudaDeviceCanAccessPeer(&can_acess, devices[i], devices[j]);
-        if (ret != cudaSuccess || can_acess != 1) {
+            cudaDeviceCanAccessPeer(&can_access, devices[i], devices[j]);
+        if (ret != cudaSuccess || can_access != 1) {
 #endif
           LOG(WARNING) << "Cannot enable P2P access from " << devices[i]
                        << " to " << devices[j];

paddle/fluid/framework/tensor_util.cc

@@ -710,8 +710,9 @@ void TensorFromStream(std::istream& is,
         PADDLE_THROW(platform::errors::Unimplemented(
             "XPUPlace is not supported when not compiled with XPU"));
       } else {
-        PADDLE_THROW(platform::errors::Unimplemented(
-            "CutomPlace is not supported when not compiled with CustomDevice"));
+        PADDLE_THROW(
+            platform::errors::Unimplemented("CustomPlace is not supported when "
+                                            "not compiled with CustomDevice"));
       }
 #endif
     } else {
@@ -887,7 +888,8 @@ std::ostream& print_tensor(std::ostream& os, const phi::DenseTensor& tensor) {
   auto element_num = tensor.numel();
 
   os << "  - data: [";
-  // Note: int8_t && uint8_t is typedf of char, ostream unable to print properly
+  // Note: int8_t && uint8_t is typedef of char, ostream unable to print
+  // properly
   if (typeid(int8_t) == typeid(T) || typeid(uint8_t) == typeid(T)) {
     if (element_num > 0) {
       os << signed(inspect[0]);

paddle/fluid/framework/trainer_factory.cc

@@ -26,8 +26,8 @@ namespace framework {
 
 class TrainerBase;
 
-typedef std::shared_ptr<TrainerBase> (*CreatetrainerFunction)();
-typedef std::unordered_map<std::string, CreatetrainerFunction> trainerMap;
+typedef std::shared_ptr<TrainerBase> (*CreateTrainerFunction)();
+typedef std::unordered_map<std::string, CreateTrainerFunction> trainerMap;
 trainerMap g_trainer_map;
 
 #define REGISTER_TRAINER_CLASS(trainer_class)                   \

paddle/fluid/operators/cvm_op.cc

@@ -127,7 +127,7 @@ class CVMOpMaker : public framework::OpProtoAndCheckerMaker {
     AddInput("X",
              "(LodTensor, default LodTensor<float>), a 2-D tensor with shape "
              "[N x D],"
-             " where N is the batch size and D is the emebdding dim. ");
+             " where N is the batch size and D is the embedding dim. ");
     AddInput("CVM",
              "(Tensor),  a 2-D Tensor with shape [N x 2], where N is the batch "
              "size, 2 is show and click.");

paddle/fluid/platform/float16_test.cu

@@ -282,7 +282,7 @@ TEST(float16, compound_on_gpu) {
   TestDivAssign(6, 2, 3);
 }
 
-TEST(float16, comparision_on_gpu) {
+TEST(float16, comparison_on_gpu) {
   TestEqual(1, 1, true);
   TestEqual(1, 2, false);
   TestNotEqual(2, 3, true);

paddle/fluid/prim/api/manual_prim/utils/utils.h

@@ -29,15 +29,15 @@ namespace prim {
 // We put some api like utils here
 template <typename T>
 Tensor empty(const paddle::experimental::IntArray& shape,
-             phi::DataType dype,
+             phi::DataType dtype,
              const paddle::Place& place);
 
 template <typename T>
 Tensor empty_like(const Tensor& x,
                   phi::DataType dtype,
                   const paddle::Place& place);
 
-// copy tensor for output ptr, in static need use assigh op
+// copy tensor for output ptr, in static need use assign op
 template <typename T>
 void by_pass(const Tensor& x, Tensor* out);
 
@@ -114,7 +114,7 @@ static std::vector<DST_T> unsafe_vector_cast(const std::vector<SRC_T>& src) {
   return dst;
 }
 
-// This fucction compute unsqueeze dims for reshape to replace unsqueeze.
+// This function compute unsqueeze dims for reshape to replace unsqueeze.
 static std::vector<int64_t> get_unsqueeze_dims(
     const Tensor& origin, const std::vector<int64_t>& axis) {
   auto origin_dims = origin.shape();

paddle/phi/kernels/prior_box_kernel.h

@@ -35,25 +35,25 @@ void PriorBoxKernel(const Context& ctx,
                     DenseTensor* out,
                     DenseTensor* var);
 
-inline void ExpandAspectRatios(const std::vector<float>& input_aspect_ratior,
+inline void ExpandAspectRatios(const std::vector<float>& input_aspect_ratio,
                                bool flip,
-                               std::vector<float>* output_aspect_ratior) {
+                               std::vector<float>* output_aspect_ratio) {
   constexpr float epsilon = 1e-6;
-  output_aspect_ratior->clear();
-  output_aspect_ratior->push_back(1.0f);
-  for (size_t i = 0; i < input_aspect_ratior.size(); ++i) {
-    float ar = input_aspect_ratior[i];
+  output_aspect_ratio->clear();
+  output_aspect_ratio->push_back(1.0f);
+  for (size_t i = 0; i < input_aspect_ratio.size(); ++i) {
+    float ar = input_aspect_ratio[i];
     bool already_exist = false;
-    for (size_t j = 0; j < output_aspect_ratior->size(); ++j) {
-      if (fabs(ar - output_aspect_ratior->at(j)) < epsilon) {
+    for (size_t j = 0; j < output_aspect_ratio->size(); ++j) {
+      if (fabs(ar - output_aspect_ratio->at(j)) < epsilon) {
         already_exist = true;
         break;
       }
     }
     if (!already_exist) {
-      output_aspect_ratior->push_back(ar);
+      output_aspect_ratio->push_back(ar);
       if (flip) {
-        output_aspect_ratior->push_back(1.0f / ar);
+        output_aspect_ratio->push_back(1.0f / ar);
       }
     }
   }